Overview

Dataset statistics

Number of variables29
Number of observations714
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1004.9 KiB
Average record size in memory1.4 KiB

Variable types

Numeric5
Categorical18
Unsupported1
DateTime4
URL1

Warnings

onsale_web has constant value "1" Constant
virtual has constant value "0" Constant
downloadable has constant value "0" Constant
rating_count has constant value "0" Constant
average_rating has constant value "0.0" Constant
tax_status has constant value "taxable" Constant
post_status has constant value "publish" Constant
comment_status has constant value "closed" Constant
ping_status has constant value "closed" Constant
post_parent has constant value "0.0" Constant
menu_order has constant value "0.0" Constant
post_type has constant value "product" Constant
comment_count has constant value "0.0" Constant
post_title has a high cardinality: 711 distinct values High cardinality
post_excerpt has a high cardinality: 677 distinct values High cardinality
post_name has a high cardinality: 714 distinct values High cardinality
df_index is highly correlated with product_idHigh correlation
product_id is highly correlated with df_indexHigh correlation
df_index is highly correlated with product_idHigh correlation
product_id is highly correlated with df_indexHigh correlation
df_index is highly correlated with product_idHigh correlation
product_id is highly correlated with df_indexHigh correlation
stock_quantity is highly correlated with total_salesHigh correlation
total_sales is highly correlated with stock_quantityHigh correlation
product_id is highly correlated with df_indexHigh correlation
df_index is highly correlated with product_idHigh correlation
comment_status is highly correlated with post_type and 13 other fieldsHigh correlation
post_type is highly correlated with comment_status and 13 other fieldsHigh correlation
post_status is highly correlated with comment_status and 13 other fieldsHigh correlation
ping_status is highly correlated with comment_status and 13 other fieldsHigh correlation
menu_order is highly correlated with comment_status and 13 other fieldsHigh correlation
downloadable is highly correlated with comment_status and 13 other fieldsHigh correlation
post_author is highly correlated with comment_status and 12 other fieldsHigh correlation
post_parent is highly correlated with comment_status and 13 other fieldsHigh correlation
rating_count is highly correlated with comment_status and 13 other fieldsHigh correlation
onsale_web is highly correlated with comment_status and 13 other fieldsHigh correlation
average_rating is highly correlated with comment_status and 13 other fieldsHigh correlation
comment_count is highly correlated with comment_status and 13 other fieldsHigh correlation
tax_status is highly correlated with comment_status and 13 other fieldsHigh correlation
virtual is highly correlated with comment_status and 13 other fieldsHigh correlation
stock_status is highly correlated with comment_status and 12 other fieldsHigh correlation
post_title is uniformly distributed Uniform
post_excerpt is uniformly distributed Uniform
post_name is uniformly distributed Uniform
df_index has unique values Unique
product_id has unique values Unique
post_date has unique values Unique
post_date_gmt has unique values Unique
post_name has unique values Unique
guid has unique values Unique
id_web is an unsupported type, check if it needs cleaning or further analysis Unsupported
stock_quantity has 141 (19.7%) zeros Zeros
total_sales has 329 (46.1%) zeros Zeros

Reproduction

Analysis started2021-07-23 12:27:41.417000
Analysis finished2021-07-23 12:28:25.513542
Duration44.1 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct714
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean978.7352941
Minimum0
Maximum1699
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2021-07-23T14:28:25.909472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile344.3
Q1629.5
median986
Q31342.5
95-th percentile1627.7
Maximum1699
Range1699
Interquartile range (IQR)713

Descriptive statistics

Standard deviation426.8668412
Coefficient of variation (CV)0.4361412567
Kurtosis-0.9213491066
Mean978.7352941
Median Absolute Deviation (MAD)357
Skewness-0.1451189805
Sum698817
Variance182215.3001
MonotonicityStrictly increasing
2021-07-23T14:28:26.650529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
6991
 
0.1%
6711
 
0.1%
6731
 
0.1%
6751
 
0.1%
6771
 
0.1%
15371
 
0.1%
6791
 
0.1%
14271
 
0.1%
6811
 
0.1%
Other values (704)704
98.6%
ValueCountFrequency (%)
01
0.1%
21
0.1%
41
0.1%
61
0.1%
81
0.1%
101
0.1%
121
0.1%
141
0.1%
161
0.1%
181
0.1%
ValueCountFrequency (%)
16991
0.1%
16971
0.1%
16951
0.1%
16931
0.1%
16911
0.1%
16891
0.1%
16871
0.1%
16851
0.1%
16831
0.1%
16811
0.1%

product_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct714
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5032.557423
Minimum3847
Maximum7338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2021-07-23T14:28:27.659882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3847
5-th percentile4073.65
Q14280.25
median4796
Q35710.5
95-th percentile6576.05
Maximum7338
Range3491
Interquartile range (IQR)1430.25

Descriptive statistics

Standard deviation790.5108776
Coefficient of variation (CV)0.1570793557
Kurtosis-0.7259825041
Mean5032.557423
Median Absolute Deviation (MAD)614.5
Skewness0.5718785896
Sum3593246
Variance624907.4476
MonotonicityStrictly increasing
2021-07-23T14:28:28.437117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40961
 
0.1%
47821
 
0.1%
47551
 
0.1%
47571
 
0.1%
47581
 
0.1%
47591
 
0.1%
58021
 
0.1%
65751
 
0.1%
59501
 
0.1%
67531
 
0.1%
Other values (704)704
98.6%
ValueCountFrequency (%)
38471
0.1%
38491
0.1%
38501
0.1%
40321
0.1%
40391
0.1%
40401
0.1%
40411
0.1%
40421
0.1%
40431
0.1%
40451
0.1%
ValueCountFrequency (%)
73381
0.1%
72471
0.1%
70251
0.1%
70231
0.1%
69301
0.1%
69281
0.1%
69261
0.1%
69201
0.1%
68871
0.1%
68861
0.1%

onsale_web
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.6 KiB
1
714 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters714
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1714
100.0%

Length

2021-07-23T14:28:29.910467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-23T14:28:30.311590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1714
100.0%

Most occurring characters

ValueCountFrequency (%)
1714
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number714
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1714
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common714
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1714
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII714
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1714
100.0%

price
Real number (ℝ≥0)

Distinct362
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.49313725
Minimum5.2
Maximum225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2021-07-23T14:28:30.915447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5.2
5-th percentile8.5
Q114.1
median23.55
Q342.175
95-th percentile79.605
Maximum225
Range219.8
Interquartile range (IQR)28.075

Descriptive statistics

Standard deviation27.81052492
Coefficient of variation (CV)0.8558891899
Kurtosis10.08839206
Mean32.49313725
Median Absolute Deviation (MAD)11.25
Skewness2.580901263
Sum23200.1
Variance773.4252965
MonotonicityNot monotonic
2021-07-23T14:28:31.773206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
1.3%
11.17
 
1.0%
13.57
 
1.0%
246
 
0.8%
12.86
 
0.8%
9.36
 
0.8%
9.96
 
0.8%
49.56
 
0.8%
16.36
 
0.8%
19.86
 
0.8%
Other values (352)649
90.9%
ValueCountFrequency (%)
5.21
 
0.1%
5.74
0.6%
5.84
0.6%
6.31
 
0.1%
6.53
0.4%
6.71
 
0.1%
6.82
0.3%
72
0.3%
7.13
0.4%
7.21
 
0.1%
ValueCountFrequency (%)
2251
0.1%
217.51
0.1%
191.31
0.1%
1761
0.1%
1751
0.1%
1571
0.1%
1371
0.1%
1351
0.1%
126.51
0.1%
124.81
0.1%

stock_quantity
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct126
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.7464986
Minimum0
Maximum578
Zeros141
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2021-07-23T14:28:32.467422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median12
Q335
95-th percentile112.7
Maximum578
Range578
Interquartile range (IQR)33

Descriptive statistics

Standard deviation48.01260815
Coefficient of variation (CV)1.670207173
Kurtosis32.56752508
Mean28.7464986
Median Absolute Deviation (MAD)12
Skewness4.488833468
Sum20525
Variance2305.210542
MonotonicityNot monotonic
2021-07-23T14:28:33.913761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0141
 
19.7%
132
 
4.5%
622
 
3.1%
1220
 
2.8%
219
 
2.7%
719
 
2.7%
517
 
2.4%
1117
 
2.4%
1016
 
2.2%
315
 
2.1%
Other values (116)396
55.5%
ValueCountFrequency (%)
0141
19.7%
132
 
4.5%
219
 
2.7%
315
 
2.1%
413
 
1.8%
517
 
2.4%
622
 
3.1%
719
 
2.7%
813
 
1.8%
915
 
2.1%
ValueCountFrequency (%)
5781
0.1%
3631
0.1%
2891
0.1%
2841
0.1%
2761
0.1%
2671
0.1%
2571
0.1%
2371
0.1%
2111
0.1%
2091
0.1%

stock_status
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size45.2 KiB
instock
574 
outofstock
140 

Length

Max length10
Median length7
Mean length7.588235294
Min length7

Characters and Unicode

Total characters5418
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowoutofstock
2nd rowoutofstock
3rd rowoutofstock
4th rowoutofstock
5th rowoutofstock

Common Values

ValueCountFrequency (%)
instock574
80.4%
outofstock140
 
19.6%

Length

2021-07-23T14:28:35.544556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-23T14:28:36.117718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
instock574
80.4%
outofstock140
 
19.6%

Most occurring characters

ValueCountFrequency (%)
o994
18.3%
t854
15.8%
s714
13.2%
c714
13.2%
k714
13.2%
i574
10.6%
n574
10.6%
u140
 
2.6%
f140
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5418
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o994
18.3%
t854
15.8%
s714
13.2%
c714
13.2%
k714
13.2%
i574
10.6%
n574
10.6%
u140
 
2.6%
f140
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin5418
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o994
18.3%
t854
15.8%
s714
13.2%
c714
13.2%
k714
13.2%
i574
10.6%
n574
10.6%
u140
 
2.6%
f140
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII5418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o994
18.3%
t854
15.8%
s714
13.2%
c714
13.2%
k714
13.2%
i574
10.6%
n574
10.6%
u140
 
2.6%
f140
 
2.6%

id_web
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size25.3 KiB

virtual
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.6 KiB
0
714 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters714
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0714
100.0%

Length

2021-07-23T14:28:37.256557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-23T14:28:37.612441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0714
100.0%

Most occurring characters

ValueCountFrequency (%)
0714
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number714
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0714
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common714
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0714
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII714
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0714
100.0%

downloadable
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.6 KiB
0
714 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters714
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0714
100.0%

Length

2021-07-23T14:28:38.722357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-23T14:28:39.154225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0714
100.0%

Most occurring characters

ValueCountFrequency (%)
0714
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number714
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0714
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common714
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0714
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII714
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0714
100.0%

rating_count
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.6 KiB
0
714 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters714
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0714
100.0%

Length

2021-07-23T14:28:40.303080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-23T14:28:40.706956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0714
100.0%

Most occurring characters

ValueCountFrequency (%)
0714
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number714
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0714
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common714
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0714
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII714
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0714
100.0%

average_rating
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
0.0
714 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2142
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0714
100.0%

Length

2021-07-23T14:28:41.790793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-23T14:28:42.251976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0714
100.0%

Most occurring characters

ValueCountFrequency (%)
01428
66.7%
.714
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1428
66.7%
Other Punctuation714
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01428
100.0%
Other Punctuation
ValueCountFrequency (%)
.714
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2142
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01428
66.7%
.714
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2142
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01428
66.7%
.714
33.3%

total_sales
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct41
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.012605042
Minimum0
Maximum96
Zeros329
Zeros (%)46.1%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2021-07-23T14:28:42.707870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile18
Maximum96
Range96
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.518183385
Coefficient of variation (CV)2.122856173
Kurtosis37.85893763
Mean4.012605042
Median Absolute Deviation (MAD)1
Skewness5.052357092
Sum2865
Variance72.55944818
MonotonicityNot monotonic
2021-07-23T14:28:43.289174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0329
46.1%
187
 
12.2%
352
 
7.3%
240
 
5.6%
430
 
4.2%
625
 
3.5%
522
 
3.1%
719
 
2.7%
1215
 
2.1%
910
 
1.4%
Other values (31)85
 
11.9%
ValueCountFrequency (%)
0329
46.1%
187
 
12.2%
240
 
5.6%
352
 
7.3%
430
 
4.2%
522
 
3.1%
625
 
3.5%
719
 
2.7%
810
 
1.4%
910
 
1.4%
ValueCountFrequency (%)
961
0.1%
871
0.1%
621
0.1%
461
0.1%
431
0.1%
421
0.1%
411
0.1%
401
0.1%
382
0.3%
371
0.1%

tax_status
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.8 KiB
taxable
714 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters4998
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtaxable
2nd rowtaxable
3rd rowtaxable
4th rowtaxable
5th rowtaxable

Common Values

ValueCountFrequency (%)
taxable714
100.0%

Length

2021-07-23T14:28:44.838467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-23T14:28:45.287635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
taxable714
100.0%

Most occurring characters

ValueCountFrequency (%)
a1428
28.6%
t714
14.3%
x714
14.3%
b714
14.3%
l714
14.3%
e714
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4998
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1428
28.6%
t714
14.3%
x714
14.3%
b714
14.3%
l714
14.3%
e714
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin4998
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1428
28.6%
t714
14.3%
x714
14.3%
b714
14.3%
l714
14.3%
e714
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1428
28.6%
t714
14.3%
x714
14.3%
b714
14.3%
l714
14.3%
e714
14.3%

post_author
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
2.0
713 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2142
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0713
99.9%
1.01
 
0.1%

Length

2021-07-23T14:28:46.643653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-23T14:28:46.955554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.0713
99.9%
1.01
 
0.1%

Most occurring characters

ValueCountFrequency (%)
.714
33.3%
0714
33.3%
2713
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1428
66.7%
Other Punctuation714
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0714
50.0%
2713
49.9%
11
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.714
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2142
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.714
33.3%
0714
33.3%
2713
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2142
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.714
33.3%
0714
33.3%
2713
33.3%
11
 
< 0.1%

post_date
Date

UNIQUE

Distinct714
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
Minimum2018-02-08 12:58:52
Maximum2020-07-20 11:00:00
2021-07-23T14:28:47.656756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:48.633917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

post_date_gmt
Date

UNIQUE

Distinct714
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
Minimum2018-02-08 11:58:52
Maximum2020-07-20 09:00:00
2021-07-23T14:28:49.646268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:50.443367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

post_title
Categorical

HIGH CARDINALITY
UNIFORM

Distinct711
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size81.1 KiB
Clos du Mont-Olivet Châteauneuf-du-Pape 2007
 
2
Domaine Hauvette IGP Alpilles Jaspe 2017
 
2
Marc Colin Et Fils Chassagne-Montrachet Blanc Les Vide-Bourses 1er Cru 2016
 
2
Domaine Saint-Nicolas Fiefs Vendéens Rouge Reflets 2018
 
1
Argentine Alamos Catena Malbec 2017
 
1
Other values (706)
706 

Length

Max length82
Median length48
Mean length47.14005602
Min length17

Characters and Unicode

Total characters33658
Distinct characters85
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique708 ?
Unique (%)99.2%

Sample

1st rowPierre Jean Villa Saint-Joseph Préface 2018
2nd rowPierre Jean Villa Saint-Joseph Rouge Tildé 2017
3rd rowPierre Jean Villa Crozes-Hermitage Accroche Coeur 2018
4th rowPierre Jean Villa IGP Collines Rhodaniennes Gamine 2018
5th rowPierre Jean Villa Côte Rôtie Carmina 2017

Common Values

ValueCountFrequency (%)
Clos du Mont-Olivet Châteauneuf-du-Pape 20072
 
0.3%
Domaine Hauvette IGP Alpilles Jaspe 20172
 
0.3%
Marc Colin Et Fils Chassagne-Montrachet Blanc Les Vide-Bourses 1er Cru 20162
 
0.3%
Domaine Saint-Nicolas Fiefs Vendéens Rouge Reflets 20181
 
0.1%
Argentine Alamos Catena Malbec 20171
 
0.1%
Planeta Sicilia Etna Rosso 20181
 
0.1%
Domaine Schoenheitz Pinot Noir Tradition 20191
 
0.1%
Château de Villeneuve Saumur-Champigny Clos de la Bienboire 20181
 
0.1%
Domaine Clerget Echezeaux Grand Cru En Orveaux 20151
 
0.1%
Domaine Huet Vouvray Le Mont Moelleux 20151
 
0.1%
Other values (701)701
98.2%

Length

2021-07-23T14:28:51.883384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de229
 
4.5%
2017187
 
3.7%
domaine168
 
3.3%
2018150
 
3.0%
rouge114
 
2.3%
2016111
 
2.2%
blanc110
 
2.2%
cru91
 
1.8%
la91
 
1.8%
du80
 
1.6%
Other values (1052)3722
73.7%

Most occurring characters

ValueCountFrequency (%)
4339
 
12.9%
e3094
 
9.2%
a2301
 
6.8%
i1823
 
5.4%
n1792
 
5.3%
r1724
 
5.1%
o1447
 
4.3%
l1316
 
3.9%
s1294
 
3.8%
u1244
 
3.7%
Other values (75)13284
39.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter21902
65.1%
Uppercase Letter4343
 
12.9%
Space Separator4339
 
12.9%
Decimal Number2677
 
8.0%
Dash Punctuation260
 
0.8%
Other Punctuation130
 
0.4%
Other Symbol4
 
< 0.1%
Currency Symbol1
 
< 0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3094
14.1%
a2301
10.5%
i1823
 
8.3%
n1792
 
8.2%
r1724
 
7.9%
o1447
 
6.6%
l1316
 
6.0%
s1294
 
5.9%
u1244
 
5.7%
t1114
 
5.1%
Other values (26)4753
21.7%
Uppercase Letter
ValueCountFrequency (%)
C704
16.2%
M355
 
8.2%
B348
 
8.0%
P318
 
7.3%
L317
 
7.3%
R293
 
6.7%
D292
 
6.7%
G284
 
6.5%
S282
 
6.5%
V187
 
4.3%
Other values (16)963
22.2%
Decimal Number
ValueCountFrequency (%)
1682
25.5%
0681
25.4%
2676
25.3%
7196
 
7.3%
8159
 
5.9%
6113
 
4.2%
968
 
2.5%
561
 
2.3%
422
 
0.8%
319
 
0.7%
Other Punctuation
ValueCountFrequency (%)
'95
73.1%
"16
 
12.3%
.5
 
3.8%
&5
 
3.8%
;5
 
3.8%
/3
 
2.3%
?1
 
0.8%
Space Separator
ValueCountFrequency (%)
4339
100.0%
Dash Punctuation
ValueCountFrequency (%)
-260
100.0%
Other Symbol
ValueCountFrequency (%)
°4
100.0%
Currency Symbol
ValueCountFrequency (%)
1
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin26245
78.0%
Common7413
 
22.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3094
 
11.8%
a2301
 
8.8%
i1823
 
6.9%
n1792
 
6.8%
r1724
 
6.6%
o1447
 
5.5%
l1316
 
5.0%
s1294
 
4.9%
u1244
 
4.7%
t1114
 
4.2%
Other values (52)9096
34.7%
Common
ValueCountFrequency (%)
4339
58.5%
1682
 
9.2%
0681
 
9.2%
2676
 
9.1%
-260
 
3.5%
7196
 
2.6%
8159
 
2.1%
6113
 
1.5%
'95
 
1.3%
968
 
0.9%
Other values (13)144
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII33080
98.3%
Latin 1 Sup577
 
1.7%
Currency Symbols1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4339
 
13.1%
e3094
 
9.4%
a2301
 
7.0%
i1823
 
5.5%
n1792
 
5.4%
r1724
 
5.2%
o1447
 
4.4%
l1316
 
4.0%
s1294
 
3.9%
u1244
 
3.8%
Other values (63)12706
38.4%
Latin 1 Sup
ValueCountFrequency (%)
é259
44.9%
è109
18.9%
ô81
 
14.0%
â80
 
13.9%
ç14
 
2.4%
à10
 
1.7%
ê7
 
1.2%
î6
 
1.0%
ü6
 
1.0%
°4
 
0.7%
Currency Symbols
ValueCountFrequency (%)
1
100.0%

post_excerpt
Categorical

HIGH CARDINALITY
UNIFORM

Distinct677
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size382.2 KiB
Les eaux de vie naissent d'une subtile alchimie où deux éléments fondamentaux se conjuguent pour que la réussite soit présente au creux de votre verre à dégustation... D'abord on ne s'improvise pas distillateur: chez les Windholtz ce sont trois  générations qui ont accumulé le plein d'expérience dans l'art subtil de "séparer par la chaleur les principes fixes et volatiles". Ensuite entre moûts et alambic au long col, au milieu des vapeurs, c'est le savoir-faire qui préside à la transmutation mystérieuse et réfléchie destinées à sublimer et à faire chanter les fruits...
 
12
"Il s'agit là de la meilleure partie de l'appellation Santenots, autrefois classée tête de cuvée par le docteur Lavalle". <span class="font5">Voilà qui nous plonge dans le bain de cette cuvée emblématique du domaine qui fut assemblée pièce par pièce, tel un puzzle, par le comte Jules Lafon. Ces 3 ha de vignes situées sur le village de Meursault et non de Volnay, sont enracinés sur une terre rouge très argileuse qui donne vie à l’un des plus grands vins rouges de la Côte de Beaune. </span>
 
4
La couleur rouge intense annonce un belle concentration, le nez évoque les fruits rouges, légèrement épicé, la bouche d'une très grande élégance livre une texture onctueuse et soyeuse malgré une structure bien présente.
 
3
La robe est rouge vif. Le nez est très floral, sur la pivoine. En bouche, l’attaque est ample et dense avec une belle sucrosité et la jolie amertume en fin de bouche ramène une belle fraîcheur.
 
3
Un magnifique blanc 100% Roussanne avec une fraîcheur, de l'élégance et de la gourmandise. Un grand blanc du Sud !
 
2
Other values (672)
690 

Length

Max length1196
Median length261
Mean length320.0042017
Min length21

Characters and Unicode

Total characters228483
Distinct characters111
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique654 ?
Unique (%)91.6%

Sample

1st rowIl exhale un nez subtil, racé avec du poivre fin et de la tourbe. La bouche est une caresse grâce à des tanins élégants. De la haute couture.
2nd rowLes vieilles vignes lui apportent une rare profondeur. L’attaque affiche de l’élégance. La bouche est portée par un minéral saisissant et des tanins de belle qualité. Grande bouteille !
3rd rowDentelle de fruit de jeunes syrah, aux tanins légers et épicés. Hyper digeste. Un délice.
4th rowGamine représente tout le fruité et la gourmandise de la syrah. Une touche épicée et des tanins fondus lui apportent une belle complexité.
5th rowLe côte rôtie Carmina monte en puissance mais garde un milieu de bouche pulpeux aux tanins aboutis. En référence à Carmina Burana, ce Côte Rôtie associe puissance, pureté, complexité et sensualité.

Common Values

ValueCountFrequency (%)
Les eaux de vie naissent d'une subtile alchimie où deux éléments fondamentaux se conjuguent pour que la réussite soit présente au creux de votre verre à dégustation... D'abord on ne s'improvise pas distillateur: chez les Windholtz ce sont trois  générations qui ont accumulé le plein d'expérience dans l'art subtil de "séparer par la chaleur les principes fixes et volatiles". Ensuite entre moûts et alambic au long col, au milieu des vapeurs, c'est le savoir-faire qui préside à la transmutation mystérieuse et réfléchie destinées à sublimer et à faire chanter les fruits...12
 
1.7%
"Il s'agit là de la meilleure partie de l'appellation Santenots, autrefois classée tête de cuvée par le docteur Lavalle". <span class="font5">Voilà qui nous plonge dans le bain de cette cuvée emblématique du domaine qui fut assemblée pièce par pièce, tel un puzzle, par le comte Jules Lafon. Ces 3 ha de vignes situées sur le village de Meursault et non de Volnay, sont enracinés sur une terre rouge très argileuse qui donne vie à l’un des plus grands vins rouges de la Côte de Beaune. </span>4
 
0.6%
La couleur rouge intense annonce un belle concentration, le nez évoque les fruits rouges, légèrement épicé, la bouche d'une très grande élégance livre une texture onctueuse et soyeuse malgré une structure bien présente.3
 
0.4%
La robe est rouge vif. Le nez est très floral, sur la pivoine. En bouche, l’attaque est ample et dense avec une belle sucrosité et la jolie amertume en fin de bouche ramène une belle fraîcheur.3
 
0.4%
Un magnifique blanc 100% Roussanne avec une fraîcheur, de l'élégance et de la gourmandise. Un grand blanc du Sud !2
 
0.3%
Nez gracieux, très élégant avec une touche florale et un parfum de vendange entière. Il évolue sur une note d'agrume. Bouche avec du relief et une belle énergie. Il y a du muscle mais accompagné par une sensation de fruit plein et dense.2
 
0.3%
Ce Chassagne Montrachet se révèle être un vin assez expressif, aux notes minérales et d’agrumes. La bouche est généreuse et longue. C’est un vin d’une grande élégance, un digne représentant de l’appellation.2
 
0.3%
Ce Pommard a une robe d’un beau rubis, typique du pinot noir. Au nez, elle livre des arômes de cerises et de petits fruits rouges, avec une touche épicée. En bouche, nous avons de la longueur et de la persistance, pour définir un vin qui est solide et tannique.2
 
0.3%
Belle bouteille dotée d’une grande complexité aromatique. Le vin est fruité, marqué par des notes de fruits jaunes, pêche et abricot. Et il dévoile des arômes de vanille et de marzipan. La bouche est suave avec une fine acidité. Ce vin ne demande qu’à vieillir quelques années.2
 
0.3%
Récoltées entre le 15 octobre et le 30 novembre à la main, ces olives typiques de la région d'Agrigento en Sicile donnent une huile extra vierge de grande qualité.2
 
0.3%
Other values (667)680
95.2%

Length

2021-07-23T14:28:53.873376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de2056
 
5.8%
et1771
 
5.0%
la922
 
2.6%
une673
 
1.9%
div671
 
1.9%
un657
 
1.9%
des601
 
1.7%
le570
 
1.6%
est551
 
1.6%
en465
 
1.3%
Other values (4498)26310
74.6%

Most occurring characters

ValueCountFrequency (%)
33497
14.7%
e27025
 
11.8%
s13744
 
6.0%
n13363
 
5.8%
t12590
 
5.5%
i12211
 
5.3%
r12109
 
5.3%
a11990
 
5.2%
u10091
 
4.4%
o8917
 
3.9%
Other values (101)72946
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter176033
77.0%
Space Separator33696
 
14.7%
Other Punctuation8306
 
3.6%
Uppercase Letter3511
 
1.5%
Math Symbol3037
 
1.3%
Control1046
 
0.5%
Decimal Number1034
 
0.5%
Dash Punctuation953
 
0.4%
Final Punctuation663
 
0.3%
Open Punctuation75
 
< 0.1%
Other values (5)129
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e27025
15.4%
s13744
 
7.8%
n13363
 
7.6%
t12590
 
7.2%
i12211
 
6.9%
r12109
 
6.9%
a11990
 
6.8%
u10091
 
5.7%
o8917
 
5.1%
l8818
 
5.0%
Other values (32)45175
25.7%
Uppercase Letter
ValueCountFrequency (%)
L701
20.0%
C493
14.0%
U316
 
9.0%
S200
 
5.7%
A177
 
5.0%
E162
 
4.6%
B148
 
4.2%
D139
 
4.0%
R129
 
3.7%
M125
 
3.6%
Other values (18)921
26.2%
Other Punctuation
ValueCountFrequency (%)
,2305
27.8%
.2160
26.0%
"1166
14.0%
'703
 
8.5%
/629
 
7.6%
;566
 
6.8%
:564
 
6.8%
!68
 
0.8%
&65
 
0.8%
#35
 
0.4%
Other values (3)45
 
0.5%
Decimal Number
ValueCountFrequency (%)
0318
30.8%
1148
14.3%
3117
 
11.3%
4102
 
9.9%
592
 
8.9%
282
 
7.9%
662
 
6.0%
852
 
5.0%
935
 
3.4%
726
 
2.5%
Final Punctuation
ValueCountFrequency (%)
646
97.4%
»15
 
2.3%
2
 
0.3%
Math Symbol
ValueCountFrequency (%)
<1244
41.0%
>1244
41.0%
=549
18.1%
Initial Punctuation
ValueCountFrequency (%)
«15
83.3%
2
 
11.1%
1
 
5.6%
Space Separator
ValueCountFrequency (%)
33497
99.4%
 199
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
-953
100.0%
Control
ValueCountFrequency (%)
1046
100.0%
Open Punctuation
ValueCountFrequency (%)
(75
100.0%
Close Punctuation
ValueCountFrequency (%)
)75
100.0%
Connector Punctuation
ValueCountFrequency (%)
_30
100.0%
Other Symbol
ValueCountFrequency (%)
°5
100.0%
Other Number
ValueCountFrequency (%)
½1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin179544
78.6%
Common48939
 
21.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e27025
15.1%
s13744
 
7.7%
n13363
 
7.4%
t12590
 
7.0%
i12211
 
6.8%
r12109
 
6.7%
a11990
 
6.7%
u10091
 
5.6%
o8917
 
5.0%
l8818
 
4.9%
Other values (60)48686
27.1%
Common
ValueCountFrequency (%)
33497
68.4%
,2305
 
4.7%
.2160
 
4.4%
<1244
 
2.5%
>1244
 
2.5%
"1166
 
2.4%
1046
 
2.1%
-953
 
1.9%
'703
 
1.4%
646
 
1.3%
Other values (31)3975
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII221175
96.8%
Latin 1 Sup6634
 
2.9%
Punctuation667
 
0.3%
Latin Ext A7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
33497
15.1%
e27025
12.2%
s13744
 
6.2%
n13363
 
6.0%
t12590
 
5.7%
i12211
 
5.5%
r12109
 
5.5%
a11990
 
5.4%
u10091
 
4.6%
o8917
 
4.0%
Other values (73)65638
29.7%
Latin 1 Sup
ValueCountFrequency (%)
é4278
64.5%
è741
 
11.2%
à471
 
7.1%
ô261
 
3.9%
î215
 
3.2%
 199
 
3.0%
û137
 
2.1%
ê115
 
1.7%
â109
 
1.6%
ù36
 
0.5%
Other values (12)72
 
1.1%
Punctuation
ValueCountFrequency (%)
646
96.9%
16
 
2.4%
2
 
0.3%
2
 
0.3%
1
 
0.1%
Latin Ext A
ValueCountFrequency (%)
œ7
100.0%

post_status
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.8 KiB
publish
714 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters4998
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpublish
2nd rowpublish
3rd rowpublish
4th rowpublish
5th rowpublish

Common Values

ValueCountFrequency (%)
publish714
100.0%

Length

2021-07-23T14:28:55.741118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-23T14:28:56.180973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
publish714
100.0%

Most occurring characters

ValueCountFrequency (%)
p714
14.3%
u714
14.3%
b714
14.3%
l714
14.3%
i714
14.3%
s714
14.3%
h714
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4998
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p714
14.3%
u714
14.3%
b714
14.3%
l714
14.3%
i714
14.3%
s714
14.3%
h714
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin4998
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
p714
14.3%
u714
14.3%
b714
14.3%
l714
14.3%
i714
14.3%
s714
14.3%
h714
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p714
14.3%
u714
14.3%
b714
14.3%
l714
14.3%
i714
14.3%
s714
14.3%
h714
14.3%

comment_status
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
closed
714 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters4284
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowclosed
2nd rowclosed
3rd rowclosed
4th rowclosed
5th rowclosed

Common Values

ValueCountFrequency (%)
closed714
100.0%

Length

2021-07-23T14:28:57.131883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-23T14:28:57.613173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
closed714
100.0%

Most occurring characters

ValueCountFrequency (%)
c714
16.7%
l714
16.7%
o714
16.7%
s714
16.7%
e714
16.7%
d714
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4284
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c714
16.7%
l714
16.7%
o714
16.7%
s714
16.7%
e714
16.7%
d714
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin4284
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
c714
16.7%
l714
16.7%
o714
16.7%
s714
16.7%
e714
16.7%
d714
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4284
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c714
16.7%
l714
16.7%
o714
16.7%
s714
16.7%
e714
16.7%
d714
16.7%

ping_status
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
closed
714 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters4284
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowclosed
2nd rowclosed
3rd rowclosed
4th rowclosed
5th rowclosed

Common Values

ValueCountFrequency (%)
closed714
100.0%

Length

2021-07-23T14:28:58.377625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-23T14:28:58.545586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
closed714
100.0%

Most occurring characters

ValueCountFrequency (%)
c714
16.7%
l714
16.7%
o714
16.7%
s714
16.7%
e714
16.7%
d714
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4284
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c714
16.7%
l714
16.7%
o714
16.7%
s714
16.7%
e714
16.7%
d714
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin4284
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
c714
16.7%
l714
16.7%
o714
16.7%
s714
16.7%
e714
16.7%
d714
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4284
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c714
16.7%
l714
16.7%
o714
16.7%
s714
16.7%
e714
16.7%
d714
16.7%

post_name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct714
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size70.1 KiB
chateau-de-la-selve-igp-coteaux-de-lardeche-st-regis-blanc-2019
 
1
domaine-hauvette-igp-alpilles-jaspe-2017-2
 
1
gilles-robin-crozes-hermitage-marelles-2018
 
1
domaine-giudicelli-patrimonio-rouge-2016
 
1
coteaux-champenois-egly-ouriet-ambonnay-rouge-2016
 
1
Other values (709)
709 

Length

Max length75
Median length43
Mean length43.32072829
Min length17

Characters and Unicode

Total characters30931
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique714 ?
Unique (%)100.0%

Sample

1st rowpierre-jean-villa-saint-joseph-preface-2018
2nd rowpierre-jean-villa-saint-joseph-tilde-2017
3rd rowpierre-jean-villa-croze-hermitage-accroche-coeur-2018
4th rowpierre-jean-villa-igp-gamine-2018
5th rowpierre-jean-villa-cote-rotie-carmina-2017

Common Values

ValueCountFrequency (%)
chateau-de-la-selve-igp-coteaux-de-lardeche-st-regis-blanc-20191
 
0.1%
domaine-hauvette-igp-alpilles-jaspe-2017-21
 
0.1%
gilles-robin-crozes-hermitage-marelles-20181
 
0.1%
domaine-giudicelli-patrimonio-rouge-20161
 
0.1%
coteaux-champenois-egly-ouriet-ambonnay-rouge-20161
 
0.1%
alphonse-mellot-sancerre-rouge-la-demoiselle-20151
 
0.1%
bernard-baudry-chinon-rouge-croix-boissee-20171
 
0.1%
champagne-larmandier-bernier-latitude1
 
0.1%
chateau-lafont-menaut-pessac-leognan-blanc-20171
 
0.1%
cognac-frapin-chateau-de-fontpinot-1989-20-ans1
 
0.1%
Other values (704)704
98.6%

Length

2021-07-23T14:28:59.706953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chateau-de-la-selve-igp-coteaux-de-lardeche-st-regis-blanc-20191
 
0.1%
domaine-hauvette-igp-alpilles-jaspe-2017-21
 
0.1%
gilles-robin-crozes-hermitage-marelles-20181
 
0.1%
domaine-giudicelli-patrimonio-rouge-20161
 
0.1%
coteaux-champenois-egly-ouriet-ambonnay-rouge-20161
 
0.1%
alphonse-mellot-sancerre-rouge-la-demoiselle-20151
 
0.1%
bernard-baudry-chinon-rouge-croix-boissee-20171
 
0.1%
champagne-larmandier-bernier-latitude1
 
0.1%
chateau-lafont-menaut-pessac-leognan-blanc-20171
 
0.1%
cognac-frapin-chateau-de-fontpinot-1989-20-ans1
 
0.1%
Other values (704)704
98.6%

Most occurring characters

ValueCountFrequency (%)
-4151
13.4%
e3223
 
10.4%
a2351
 
7.6%
r1888
 
6.1%
i1763
 
5.7%
n1711
 
5.5%
o1470
 
4.8%
l1454
 
4.7%
s1436
 
4.6%
c1291
 
4.2%
Other values (29)10193
33.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter24098
77.9%
Dash Punctuation4151
 
13.4%
Decimal Number2674
 
8.6%
Connector Punctuation6
 
< 0.1%
Other Punctuation2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3223
13.4%
a2351
 
9.8%
r1888
 
7.8%
i1763
 
7.3%
n1711
 
7.1%
o1470
 
6.1%
l1454
 
6.0%
s1436
 
6.0%
c1291
 
5.4%
t1190
 
4.9%
Other values (16)6321
26.2%
Decimal Number
ValueCountFrequency (%)
0679
25.4%
1679
25.4%
2676
25.3%
7194
 
7.3%
8162
 
6.1%
6115
 
4.3%
968
 
2.5%
560
 
2.2%
421
 
0.8%
320
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
-4151
100.0%
Connector Punctuation
ValueCountFrequency (%)
_6
100.0%
Other Punctuation
ValueCountFrequency (%)
%2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin24098
77.9%
Common6833
 
22.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3223
13.4%
a2351
 
9.8%
r1888
 
7.8%
i1763
 
7.3%
n1711
 
7.1%
o1470
 
6.1%
l1454
 
6.0%
s1436
 
6.0%
c1291
 
5.4%
t1190
 
4.9%
Other values (16)6321
26.2%
Common
ValueCountFrequency (%)
-4151
60.7%
0679
 
9.9%
1679
 
9.9%
2676
 
9.9%
7194
 
2.8%
8162
 
2.4%
6115
 
1.7%
968
 
1.0%
560
 
0.9%
421
 
0.3%
Other values (3)28
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII30931
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-4151
13.4%
e3223
 
10.4%
a2351
 
7.6%
r1888
 
6.1%
i1763
 
5.7%
n1711
 
5.5%
o1470
 
4.8%
l1454
 
4.7%
s1436
 
4.6%
c1291
 
4.2%
Other values (29)10193
33.0%
Distinct587
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
Minimum2018-02-20 15:19:23
Maximum2020-08-27 18:55:03
2021-07-23T14:29:00.560109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:29:01.241220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct587
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
Minimum2018-02-20 14:19:23
Maximum2020-08-27 16:55:03
2021-07-23T14:29:02.160032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:29:03.049190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

post_parent
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
0.0
714 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2142
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0714
100.0%

Length

2021-07-23T14:29:04.832719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-23T14:29:05.188606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0714
100.0%

Most occurring characters

ValueCountFrequency (%)
01428
66.7%
.714
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1428
66.7%
Other Punctuation714
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01428
100.0%
Other Punctuation
ValueCountFrequency (%)
.714
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2142
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01428
66.7%
.714
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2142
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01428
66.7%
.714
33.3%

guid
URL

UNIQUE

Distinct714
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size79.6 KiB
https://www.wine-spirit.fr/?post_type=product&#038;p=6101
 
1
https://www.wine-spirit.fr/?post_type=product&#038;p=4042
 
1
https://www.wine-spirit.fr/?post_type=product&#038;p=6751
 
1
https://www.wine-spirit.fr/?post_type=product&#038;p=6617
 
1
https://www.wine-spirit.fr/?post_type=product&#038;p=4994
 
1
Other values (709)
709 
ValueCountFrequency (%)
https://www.wine-spirit.fr/?post_type=product&#038;p=61011
 
0.1%
https://www.wine-spirit.fr/?post_type=product&#038;p=40421
 
0.1%
https://www.wine-spirit.fr/?post_type=product&#038;p=67511
 
0.1%
https://www.wine-spirit.fr/?post_type=product&#038;p=66171
 
0.1%
https://www.wine-spirit.fr/?post_type=product&#038;p=49941
 
0.1%
https://www.wine-spirit.fr/?post_type=product&#038;p=49261
 
0.1%
https://www.wine-spirit.fr/?post_type=product&#038;p=50071
 
0.1%
https://www.wine-spirit.fr/?post_type=product&#038;p=42581
 
0.1%
https://www.wine-spirit.fr/?post_type=product&#038;p=47341
 
0.1%
https://www.wine-spirit.fr/?post_type=product&#038;p=66311
 
0.1%
Other values (704)704
98.6%
ValueCountFrequency (%)
https714
100.0%
ValueCountFrequency (%)
www.wine-spirit.fr714
100.0%
ValueCountFrequency (%)
/714
100.0%
ValueCountFrequency (%)
post_type=product&714
100.0%
ValueCountFrequency (%)
038;p=56091
 
0.1%
038;p=58011
 
0.1%
038;p=41621
 
0.1%
038;p=43991
 
0.1%
038;p=42271
 
0.1%
038;p=48761
 
0.1%
038;p=58911
 
0.1%
038;p=47201
 
0.1%
038;p=49151
 
0.1%
038;p=41921
 
0.1%
Other values (704)704
98.6%

menu_order
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
0.0
714 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2142
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0714
100.0%

Length

2021-07-23T14:29:06.089394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-23T14:29:06.278552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0714
100.0%

Most occurring characters

ValueCountFrequency (%)
01428
66.7%
.714
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1428
66.7%
Other Punctuation714
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01428
100.0%
Other Punctuation
ValueCountFrequency (%)
.714
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2142
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01428
66.7%
.714
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2142
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01428
66.7%
.714
33.3%

post_type
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.8 KiB
product
714 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters4998
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowproduct
2nd rowproduct
3rd rowproduct
4th rowproduct
5th rowproduct

Common Values

ValueCountFrequency (%)
product714
100.0%

Length

2021-07-23T14:29:07.491542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-23T14:29:07.867448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
product714
100.0%

Most occurring characters

ValueCountFrequency (%)
p714
14.3%
r714
14.3%
o714
14.3%
d714
14.3%
u714
14.3%
c714
14.3%
t714
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4998
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p714
14.3%
r714
14.3%
o714
14.3%
d714
14.3%
u714
14.3%
c714
14.3%
t714
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin4998
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
p714
14.3%
r714
14.3%
o714
14.3%
d714
14.3%
u714
14.3%
c714
14.3%
t714
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p714
14.3%
r714
14.3%
o714
14.3%
d714
14.3%
u714
14.3%
c714
14.3%
t714
14.3%

comment_count
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
0.0
714 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2142
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0714
100.0%

Length

2021-07-23T14:29:08.796424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-23T14:29:09.389652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0714
100.0%

Most occurring characters

ValueCountFrequency (%)
01428
66.7%
.714
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1428
66.7%
Other Punctuation714
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01428
100.0%
Other Punctuation
ValueCountFrequency (%)
.714
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2142
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01428
66.7%
.714
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2142
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01428
66.7%
.714
33.3%

Interactions

2021-07-23T14:27:57.928885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:27:58.823462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:27:59.453900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:27:59.869814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:00.607035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:01.436221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:02.220025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:03.101036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:03.794163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:04.785258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:05.602313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:06.442793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:07.122636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:07.811764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:08.568855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:09.556389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:10.457563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:11.355556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:12.155334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:12.744717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:13.040628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:13.893821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:14.641048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:15.466597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-23T14:28:16.042437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-07-23T14:29:09.735000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-23T14:29:10.740017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-23T14:29:12.032999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-23T14:29:13.161955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-23T14:29:14.071394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-23T14:28:17.488812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-23T14:28:24.713200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexproduct_idonsale_webpricestock_quantitystock_statusid_webvirtualdownloadablerating_countaverage_ratingtotal_salestax_statuspost_authorpost_datepost_date_gmtpost_titlepost_excerptpost_statuscomment_statusping_statuspost_namepost_modifiedpost_modified_gmtpost_parentguidmenu_orderpost_typecomment_count
003847124.20outofstock152980000.06.0taxable2.02018-02-08 12:58:522018-02-08 11:58:52Pierre Jean Villa Saint-Joseph Préface 2018Il exhale un nez subtil, racé avec du poivre fin et de la tourbe. La bouche est une caresse grâce à des tanins élégants. De la haute couture.publishclosedclosedpierre-jean-villa-saint-joseph-preface-20182019-12-30 09:30:292019-12-30 08:30:290.0https://www.wine-spirit.fr/?post_type=product&#038;p=38470.0product0.0
123849134.30outofstock152960000.00.0taxable2.02018-02-08 13:49:412018-02-08 12:49:41Pierre Jean Villa Saint-Joseph Rouge Tildé 2017Les vieilles vignes lui apportent une rare profondeur. L’attaque affiche de l’élégance. La bouche est portée par un minéral saisissant et des tanins de belle qualité. Grande bouteille !publishclosedclosedpierre-jean-villa-saint-joseph-tilde-20172019-12-21 09:00:172019-12-21 08:00:170.0https://www.wine-spirit.fr/?post_type=product&#038;p=38490.0product0.0
243850120.80outofstock153000000.00.0taxable2.02018-02-08 14:08:362018-02-08 13:08:36Pierre Jean Villa Crozes-Hermitage Accroche Coeur 2018Dentelle de fruit de jeunes syrah, aux tanins légers et épicés. Hyper digeste. Un délice.publishclosedclosedpierre-jean-villa-croze-hermitage-accroche-coeur-20182020-06-26 18:15:032020-06-26 16:15:030.0https://www.wine-spirit.fr/?post_type=product&#038;p=38500.0product0.0
364032114.10outofstock198140000.03.0taxable2.02018-02-09 14:01:052018-02-09 13:01:05Pierre Jean Villa IGP Collines Rhodaniennes Gamine 2018Gamine représente tout le fruité et la gourmandise de la syrah. Une touche épicée et des tanins fondus lui apportent une belle complexité.publishclosedclosedpierre-jean-villa-igp-gamine-20182020-01-04 16:36:012020-01-04 15:36:010.0https://www.wine-spirit.fr/?post_type=product&#038;p=40320.0product0.0
484039146.00outofstock198150000.00.0taxable2.02018-02-12 09:04:372018-02-12 08:04:37Pierre Jean Villa Côte Rôtie Carmina 2017Le côte rôtie Carmina monte en puissance mais garde un milieu de bouche pulpeux aux tanins aboutis. En référence à Carmina Burana, ce Côte Rôtie associe puissance, pureté, complexité et sensualité.publishclosedclosedpierre-jean-villa-cote-rotie-carmina-20172020-01-04 16:36:102020-01-04 15:36:100.0https://www.wine-spirit.fr/?post_type=product&#038;p=40390.0product0.0
5104040134.30outofstock153030000.00.0taxable2.02018-02-12 09:13:352018-02-12 08:13:35Pierre Jean Villa Saint-Joseph Saut De l'Ange 2018Roussanne finement exotique, atypique par sa vivacité, d'un grain frais et plein de croquant.publishclosedclosedpierre-jean-villa-saint-joseph-saut-ange-20182019-11-02 13:25:072019-11-02 12:25:070.0https://www.wine-spirit.fr/?post_type=product&#038;p=40400.0product0.0
6124041132.715instock149750000.00.0taxable2.02018-02-12 09:45:142018-02-12 08:45:14Pierre Gaillard Condrieu 2018Un joli nez de fruits exotiques comme le litchi, de pêche blanche et de violette. La bouche est ronde, équilibrée et promet des saveurs exotiques aussi élégantes qu’au nez.publishclosedclosedpierre-gaillard-condrieu-20182020-08-14 18:15:022020-08-14 16:15:020.0https://www.wine-spirit.fr/?post_type=product&#038;p=40410.0product0.0
7144042131.234instock160420000.07.0taxable2.02018-02-12 09:54:272018-02-12 08:54:27Pierre Gaillard Cornas 2017Une jolie robe grenat avec des reflets violacés. Un  nez de petits fruits noirs et de notes épicées. Belle structure, tanins aboutis et beaucoup de finesse.publishclosedclosedpierre-gaillard-cornas-20172020-08-14 10:15:022020-08-14 08:15:020.0https://www.wine-spirit.fr/?post_type=product&#038;p=40420.0product0.0
8164043160.012instock149800000.03.0taxable2.02018-02-12 10:03:052018-02-12 09:03:05Pierre Gaillard Côte Rôtie Esprit de Blonde 2017Complexité, finesse et subtilité sont au rendez-vous. Concentration et longueur également! Une cuvée à garder quelques années.publishclosedclosedpierre-gaillard-cote-rotie-esprit-blond-20172020-08-24 14:00:032020-08-24 12:00:030.0https://www.wine-spirit.fr/?post_type=product&#038;p=40430.0product0.0
9184045142.666instock160410000.014.0taxable2.02018-02-12 10:09:032018-02-12 09:09:03Pierre Gaillard Côte Rôtie 2018Ce vin exprime la diversité et l'équilibre entre puissance et élégance des différentes parcelles de Côte Rôtie. Fruité fin et charnu.publishclosedclosedpierre-gaillard-cote-rotie-20182020-08-03 09:55:032020-08-03 07:55:030.0https://www.wine-spirit.fr/?post_type=product&#038;p=40450.0product0.0

Last rows

df_indexproduct_idonsale_webpricestock_quantitystock_statusid_webvirtualdownloadablerating_countaverage_ratingtotal_salestax_statuspost_authorpost_datepost_date_gmtpost_titlepost_excerptpost_statuscomment_statusping_statuspost_namepost_modifiedpost_modified_gmtpost_parentguidmenu_orderpost_typecomment_count
70416816886142.00outofstock148970000.00.0taxable2.02020-04-24 21:18:322020-04-24 19:18:32Gratavinum Priorat GV5 2011Couleur grenat très foncé, avec des reflets. Un nez expressif de notes de confiture de fruits noirs et de notes minérales, crayon de graphite très intenses. En bouche, il montre beaucoup de tanins présents mais aussi une finesse d’une qualité extraordinaire. Haute concentration dt volume, excellente acidité, la bouche est entièrement marquée par le caractère minéral intense qui conduit à une finale très longue et une grande sensation de fraîcheur.publishclosedclosedgratavinum-priorat-gv5-20112020-06-26 15:05:032020-06-26 13:05:030.0https://www.wine-spirit.fr/?post_type=product&#038;p=68860.0product0.0
70516836887121.824instock157360000.00.0taxable2.02020-04-24 21:32:592020-04-24 19:32:59Gratavinum Priorat 2?r 2017Le nez est intense, avec des notes de confiture de fruits rouges, des notes minérales, et des notes d’herbes sauvages. Les tanins sont mûrs mais très équilibrés, combinés avec un bon volume et une bonne acidité en bouche. Les arômes fruités font ensuite place à une finition nettement minérale, avec une longue finale et une sensation de fraîcheur.publishclosedclosedgratavinum-priorat-2%cf%80r-20172020-06-24 11:45:032020-06-24 09:45:030.0https://www.wine-spirit.fr/?post_type=product&#038;p=68870.0product0.0
70616856920150.51instock157400000.00.0taxable2.02020-04-25 12:32:172020-04-25 10:32:17Château Jean Faure Saint-Emilion Grand Cru 2015Une réussite absolue, jamais les cabernets ont autant « parlé » sur ce magnifique terroir. Olivier Decelle récolte les fruits de ses efforts sans relâche, et de ses intuitions, pour hisser le cru au sommet du plateau. Un vin racé, vertical, plein de fraîcheur et d’un fruité bluffant.publishclosedclosedjean-faure-saint-emilion-grand-cru-20152020-08-27 11:35:022020-08-27 09:35:020.0https://www.wine-spirit.fr/?post_type=product&#038;p=69200.0product0.0
70716876926149.924instock158450000.01.0taxable2.02020-04-25 12:43:232020-04-25 10:43:23Château Jean Faure Saint-Emilion Grand Cru 2016Velouté, profond, racé, beaucoup de sève. Très belle fraîcheur de fruit, tanin fin et serré, allonge svelte, superbe.publishclosedclosedchateau-jean-faure-saint-emilion-grand-cru-20162020-07-20 17:09:232020-07-20 15:09:230.0https://www.wine-spirit.fr/?post_type=product&#038;p=69260.0product0.0
70816896928119.020instock157410000.02.0taxable2.02020-04-25 12:49:492020-04-25 10:49:49Le Cèdre de Jean Faure Saint-Emilion 2016Un nez ouvert  sur un velouté de fruits rouges, livrant avec beaucoup de douceur et de finesse quelques senteurs de cerise et de fraise sur un lit de petites épices.En bouche nous avons le même esprit et même plaisir. Structure fine et élégante.publishclosedclosedcedre-de-jean-faure-saint-emilion-20162020-08-27 15:15:022020-08-27 13:15:020.0https://www.wine-spirit.fr/?post_type=product&#038;p=69280.0product0.0
7091691693018.483instock161350000.05.0taxable2.02020-04-25 13:22:382020-04-25 11:22:38Mouthes Le Bihan Côtes de Duras L'Aimé Chai 2015Belle robe jeune à dominante rubis soutenue. Nez fruité, avec des notes de fruits  confits, de confiture, de fruits noirs, épicé, poivré et mentholé. Bouche charnue, vin plein, dense, fruité, fumé, finissant sur des tanins tactiles et croquants Un vin de repas entre copains!publishclosedclosedmouthes-le-bihan-aime-chai-20152020-08-26 17:35:032020-08-26 15:35:030.0https://www.wine-spirit.fr/?post_type=product&#038;p=69300.0product0.0
71016937023127.515instock158910000.00.0taxable2.02020-05-02 14:53:402020-05-02 12:53:40Camin Larredya Jurançon Sec La Virada 2018L'exotisme du nez est complété par d'élégantes et complexes notes de pistache et d'amande amère. La fraîcheur apparaît tendue, millimétrée, et empreinte de pureté. La mandarine, l'orange, les fruits exotiques apportent des arômes gourmands. L'allonge s'étire sans perdre d'éclat. Un modèle de précision.publishclosedclosedcamin-larredya-jurancon-sec-la-virada-20182020-08-26 17:35:022020-08-26 15:35:020.0https://www.wine-spirit.fr/?post_type=product&#038;p=70230.0product0.0
71116957025169.02instock158870000.00.0taxable2.02020-05-02 15:00:542020-05-02 13:00:54Domaine Jamet Côte Rôtie Fructus Voluptas 2018Pour cette cuvée, Jean-Paul Jamet recherche un plaisir sur le fruit plus immédiat tout en conservant un potentiel de garde.\n\n&nbsp;publishclosedclosedjamet-cote-rotie-fructus-voluptas-20182020-08-14 18:15:032020-08-14 16:15:030.0https://www.wine-spirit.fr/?post_type=product&#038;p=70250.0product0.0
71216977247154.823instock13127-10000.00.0taxable2.02020-06-09 15:42:042020-06-09 13:42:04Clos du Mont-Olivet Châteauneuf-du-Pape 2007Nez gracieux, très élégant avec une touche florale et un parfum de vendange entière. Il évolue sur une note d'agrume. Bouche avec du relief et une belle énergie. Il y a du muscle mais accompagné par une sensation de fruit plein et dense.publishclosedclosedclos-du-mont-olivet-chateauneuf-du-pape-2007-22020-07-20 17:09:062020-07-20 15:09:060.0https://www.wine-spirit.fr/?post_type=product&#038;p=72470.0product0.0
71316997338116.345instock162300000.00.0taxable2.02020-07-20 11:00:002020-07-20 09:00:00Domaine Saint-Nicolas Vin de France Blanc Les Clous 2019Issu d'un assemblage de chenin blanc et de chardonnay, ce vin présente des notes iodées ainsi que des arômes de fleurs blanches. En bouche, un vin d'une grande tension se dévoile, avec des saveurs salées et une fine amertume.publishclosedcloseddomaine-saint-nicolas-fiefs-vendeens-blanc-les-clous-20192020-08-13 10:45:032020-08-13 08:45:030.0https://www.wine-spirit.fr/?post_type=product&#038;p=73380.0product0.0